1. Technical Field
The present invention relates to an improved method and apparatus for filtering noise to detect downhole events. More specifically, the invention utilizes a neural-network based filtering system to detect faint signals from within a noisy environment such as an oil well.
2. Description of the Related Art
It is important to know if a perforation gun has detonated completely, partially, or not at all. However, this can be difficult to know with absolute certainty when the gun has been lowered several thousand feet into an oil well. The fluid in the well can dampen the vibration from the detonation. The sound from the detonation can be detected and amplified. However. the amplified signal can still be undecipherable because it is intermixed with the random and periodic noises produced around the well. Noise sources can include engines and pumps, the sound of fluid passing through pipes, and the impact of metal tools on pipes. A need exists for an improved method of filtering the noise from the signal so that the undetonated or partially detonated gun is safely handled.
In addition to perforation guns, it is also desirable to monitor many downhole events, such as the actuation of a device, the latching of two interconnecting devices, or the breaking of shear pins. In each case, a distinctive sound or signal will be produced. But, detecting this signal will be hampered by the inherent noise of the environment. And while the examples given so far involve detection of the signal at the surface of a downhole event, the need may be reversed so that an intelligent downhole device can adequately detect a signal initiated at the surface.
A need exists for an improved method of filtering the noise from the desired signal. Such a method should include an adaptive method of correcting the error from earlier filtering efforts, in essence tuning itself to the needs of the particular well and surface environments.